Visualization of single cell phenotyping

Recent developments in image-based single cell screens have helped to interrogate human cellular biology. However, visualization of such diverse datasets remains a major challenge for deriving biologically meaningful visualizations. Here, we present novel visualizations of a kinome-wide RNAi screen in human induced pluripotent stem cells (IPSC). In our approach an unsupervised, data driven characterization of cell populations is achieved by a k-means clustering in 6 distinct phenotypic classes. Using the resulting training dataset as a template, every other cell in the screen was classified using a k-nearest-neighbor-classifier and 8 cellular features. As a result, the distribution of cells among the different populations could be counted for each treatment similarly as is been done in the field of flow cytometry with the advantage of using cell morphological, neighborhood and texture features of adherent cells. This approach is especially useful as it transforms high dimensional single cell feature data into a format well known in cell biology and clinical research. This novel style of visualization enables for new biological discoveries by high throughput single cell screens.